{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from sklearn import metrics\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "pd.set_option('display.max_columns',1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier,plot_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_value_labels(axis,format):\n",
    "\n",
    "    rects = axis.patches\n",
    "\n",
    "    # For each bar: Place a label\n",
    "    for rect in rects:\n",
    "\n",
    "        # Get X and Y placement of label from rect.\n",
    "        y_value = rect.get_height()\n",
    "        x_value = rect.get_x() + rect.get_width() / 2\n",
    "\n",
    "        label = '{:.2f}'.format(y_value)\n",
    "\n",
    "        # Vertical alignment for positive values\n",
    "        va = 'bottom'\n",
    "\n",
    "        # If value of bar is negative: Place label below bar\n",
    "        if y_value < 0:\n",
    "            # Invert space to place label below\n",
    "            space *= -1\n",
    "            # Vertically align label at top\n",
    "            va = 'top'\n",
    "\n",
    "        # Create annotation\n",
    "\n",
    "        axis.annotate(label, (x_value, y_value), \n",
    "                      xytext=(0, 2), \n",
    "                      textcoords=\"offset points\", \n",
    "                      ha='center', \n",
    "                      rotation=45, \n",
    "                      va=va)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('data/credit-card-default/data.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>SEX</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>MARRIAGE</td>\n",
       "      <td>AGE</td>\n",
       "      <td>PAY_0</td>\n",
       "      <td>PAY_2</td>\n",
       "      <td>PAY_3</td>\n",
       "      <td>PAY_4</td>\n",
       "      <td>PAY_5</td>\n",
       "      <td>PAY_6</td>\n",
       "      <td>BILL_AMT1</td>\n",
       "      <td>BILL_AMT2</td>\n",
       "      <td>BILL_AMT3</td>\n",
       "      <td>BILL_AMT4</td>\n",
       "      <td>BILL_AMT5</td>\n",
       "      <td>BILL_AMT6</td>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>default payment next month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           X1   X2         X3        X4   X5     X6     X7     X8     X9  \\\n",
       "ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4   \n",
       "1       20000    2          2         1   24      2      2     -1     -1   \n",
       "2      120000    2          2         2   26     -1      2      0      0   \n",
       "3       90000    2          2         2   34      0      0      0      0   \n",
       "4       50000    2          2         1   37      0      0      0      0   \n",
       "\n",
       "      X10    X11        X12        X13        X14        X15        X16  \\\n",
       "ID  PAY_5  PAY_6  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5   \n",
       "1      -2     -2       3913       3102        689          0          0   \n",
       "2       0      2       2682       1725       2682       3272       3455   \n",
       "3       0      0      29239      14027      13559      14331      14948   \n",
       "4       0      0      46990      48233      49291      28314      28959   \n",
       "\n",
       "          X17       X18       X19       X20       X21       X22       X23  \\\n",
       "ID  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6   \n",
       "1           0         0       689         0         0         0         0   \n",
       "2        3261         0      1000      1000      1000         0      2000   \n",
       "3       15549      1518      1500      1000      1000      1000      5000   \n",
       "4       29547      2000      2019      1200      1100      1069      1000   \n",
       "\n",
       "                             Y  \n",
       "ID  default payment next month  \n",
       "1                            1  \n",
       "2                            1  \n",
       "3                            0  \n",
       "4                            0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={\n",
    "    'X1':'limit',\n",
    "    'X2':'sex',\n",
    "    'X3':'education',\n",
    "    'X4':'marriage',\n",
    "    'X5':'age',\n",
    "    'X6': 'status_200509',\n",
    "    'X7': 'status_200508',\n",
    "    'X8': 'status_200507',\n",
    "    'X9': 'status_200506',\n",
    "    'X10': 'status_200505',\n",
    "    'X11': 'status_200504',\n",
    "    \n",
    "    'X12': 'amount_charged_200509',\n",
    "    'X13': 'amount_charged_200508',\n",
    "    'X14': 'amount_charged_200507',\n",
    "    'X15': 'amount_charged_200506',\n",
    "    'X16': 'amount_charged_200505',\n",
    "    'X17': 'amount_charged_200504',\n",
    "    \n",
    "    'X18': 'amount_paid_200509',\n",
    "    'X19': 'amount_paid_200508',\n",
    "    'X20': 'amount_paid_200507',\n",
    "    'X21': 'amount_paid_200506',\n",
    "    'X22': 'amount_paid_200505',\n",
    "    'X23': 'amount_paid_200504',\n",
    "    'Y': 'default'\n",
    "}).reset_index().drop([0]).drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>limit</th>\n",
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       "      <th>amount_charged_200506</th>\n",
       "      <th>amount_charged_200505</th>\n",
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       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13126</th>\n",
       "      <td>400000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>34</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
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       "      <td>0</td>\n",
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       "      <th>14636</th>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>66122</td>\n",
       "      <td>92131</td>\n",
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       "      <td>43182</td>\n",
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       "      <td>2600</td>\n",
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       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19430</th>\n",
       "      <td>200000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2317</td>\n",
       "      <td>7588</td>\n",
       "      <td>7606</td>\n",
       "      <td>14053</td>\n",
       "      <td>0</td>\n",
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       "      <td>7588</td>\n",
       "      <td>7614</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>1140</td>\n",
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       "      <td>800</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>70000</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7655</td>\n",
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       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17046</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4541</td>\n",
       "      <td>0</td>\n",
       "      <td>724</td>\n",
       "      <td>18589</td>\n",
       "      <td>18985</td>\n",
       "      <td>19531</td>\n",
       "      <td>0</td>\n",
       "      <td>724</td>\n",
       "      <td>18589</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>350000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6015</td>\n",
       "      <td>8751</td>\n",
       "      <td>686</td>\n",
       "      <td>430667</td>\n",
       "      <td>161089</td>\n",
       "      <td>119339</td>\n",
       "      <td>8797</td>\n",
       "      <td>691</td>\n",
       "      <td>238241</td>\n",
       "      <td>1009</td>\n",
       "      <td>596</td>\n",
       "      <td>10356</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13640</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11995</td>\n",
       "      <td>13324</td>\n",
       "      <td>14029</td>\n",
       "      <td>14308</td>\n",
       "      <td>14625</td>\n",
       "      <td>18245</td>\n",
       "      <td>1528</td>\n",
       "      <td>1233</td>\n",
       "      <td>512</td>\n",
       "      <td>547</td>\n",
       "      <td>3880</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9469</th>\n",
       "      <td>200000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>935</td>\n",
       "      <td>1418</td>\n",
       "      <td>-2</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "      <td>928</td>\n",
       "      <td>1418</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>300</td>\n",
       "      <td>928</td>\n",
       "      <td>464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18652</th>\n",
       "      <td>100000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118008</td>\n",
       "      <td>116686</td>\n",
       "      <td>113939</td>\n",
       "      <td>111079</td>\n",
       "      <td>107257</td>\n",
       "      <td>105377</td>\n",
       "      <td>5614</td>\n",
       "      <td>5600</td>\n",
       "      <td>5500</td>\n",
       "      <td>4500</td>\n",
       "      <td>5000</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12360</th>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13639</td>\n",
       "      <td>14711</td>\n",
       "      <td>15462</td>\n",
       "      <td>16004</td>\n",
       "      <td>16203</td>\n",
       "      <td>16916</td>\n",
       "      <td>1600</td>\n",
       "      <td>1300</td>\n",
       "      <td>800</td>\n",
       "      <td>605</td>\n",
       "      <td>1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41402</td>\n",
       "      <td>41742</td>\n",
       "      <td>42758</td>\n",
       "      <td>43510</td>\n",
       "      <td>44420</td>\n",
       "      <td>45319</td>\n",
       "      <td>1300</td>\n",
       "      <td>2010</td>\n",
       "      <td>1762</td>\n",
       "      <td>1762</td>\n",
       "      <td>1790</td>\n",
       "      <td>1622</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7013</th>\n",
       "      <td>180000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>4358</td>\n",
       "      <td>3160</td>\n",
       "      <td>2611</td>\n",
       "      <td>8710</td>\n",
       "      <td>5127</td>\n",
       "      <td>8758</td>\n",
       "      <td>3169</td>\n",
       "      <td>2618</td>\n",
       "      <td>8736</td>\n",
       "      <td>5142</td>\n",
       "      <td>8766</td>\n",
       "      <td>15040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5223</th>\n",
       "      <td>400000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>36560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1757</td>\n",
       "      <td>21667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1757</td>\n",
       "      <td>20000</td>\n",
       "      <td>10158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        limit sex education marriage age status_200509 status_200508  \\\n",
       "13126  400000   1         1        1  34            -2            -2   \n",
       "14636   80000   1         2        2  34             0             0   \n",
       "19430  200000   2         3        1  49             1            -2   \n",
       "4382    20000   2         2        1  41            -1            -1   \n",
       "7660    70000   2         1        1  36             2             0   \n",
       "10638   50000   1         2        2  24             0             0   \n",
       "17046   20000   2         1        2  25            -1            -1   \n",
       "24979  350000   1         1        2  33            -2            -2   \n",
       "13640   20000   2         2        2  23             2             0   \n",
       "9469   200000   2         1        1  47            -1            -1   \n",
       "18652  100000   2         2        1  29             0             0   \n",
       "12360   20000   1         1        2  40             0             0   \n",
       "54     180000   2         1        2  25             1             2   \n",
       "7013   180000   1         3        2  29            -2            -2   \n",
       "5223   400000   1         3        2  49             0             0   \n",
       "\n",
       "      status_200507 status_200506 status_200505 status_200504  \\\n",
       "13126            -2            -2            -2            -2   \n",
       "14636             0             0             0             0   \n",
       "19430            -1            -1            -1            -1   \n",
       "4382             -1            -1            -1            -1   \n",
       "7660              0             0             0             0   \n",
       "10638             0             0             0             0   \n",
       "17046            -1            -1             0             0   \n",
       "24979            -2            -1             0             0   \n",
       "13640             0             0             0             0   \n",
       "9469             -1            -1            -1            -1   \n",
       "18652             0             0             0             0   \n",
       "12360             0             0             0             0   \n",
       "54                0             0             0             0   \n",
       "7013             -2            -2            -2            -1   \n",
       "5223             -2            -2            -1             0   \n",
       "\n",
       "      amount_charged_200509 amount_charged_200508 amount_charged_200507  \\\n",
       "13126                     0                     0                     0   \n",
       "14636                 66122                 92131                 47655   \n",
       "19430                     0                     0                  2317   \n",
       "4382                   2468                  1077                  1140   \n",
       "7660                  81719                 85389                 86287   \n",
       "10638                 23553                 21507                 17827   \n",
       "17046                  4541                     0                   724   \n",
       "24979                  6015                  8751                   686   \n",
       "13640                 11995                 13324                 14029   \n",
       "9469                    935                  1418                    -2   \n",
       "18652                118008                116686                113939   \n",
       "12360                 13639                 14711                 15462   \n",
       "54                    41402                 41742                 42758   \n",
       "7013                   4358                  3160                  2611   \n",
       "5223                  36560                     0                     0   \n",
       "\n",
       "      amount_charged_200506 amount_charged_200505 amount_charged_200504  \\\n",
       "13126                     0                     0                     0   \n",
       "14636                 43182                 44332                 45440   \n",
       "19430                  7588                  7606                 14053   \n",
       "4382                      0                  7014                  7696   \n",
       "7660                  65287                 35345                  9360   \n",
       "10638                  7655                  7881                  8248   \n",
       "17046                 18589                 18985                 19531   \n",
       "24979                430667                161089                119339   \n",
       "13640                 14308                 14625                 18245   \n",
       "9469                    120                   150                   928   \n",
       "18652                111079                107257                105377   \n",
       "12360                 16004                 16203                 16916   \n",
       "54                    43510                 44420                 45319   \n",
       "7013                   8710                  5127                  8758   \n",
       "5223                      0                  1757                 21667   \n",
       "\n",
       "      amount_paid_200509 amount_paid_200508 amount_paid_200507  \\\n",
       "13126                  0                  0                  0   \n",
       "14636               2600               4300               2000   \n",
       "19430                  0               2317               7588   \n",
       "4382                1087               1140                  0   \n",
       "7660                5000               3000               2000   \n",
       "10638               1700               2000                500   \n",
       "17046                  0                724              18589   \n",
       "24979               8797                691             238241   \n",
       "13640               1528               1233                512   \n",
       "9469                1418                  0                122   \n",
       "18652               5614               5600               5500   \n",
       "12360               1600               1300                800   \n",
       "54                  1300               2010               1762   \n",
       "7013                3169               2618               8736   \n",
       "5223                   0                  0                  0   \n",
       "\n",
       "      amount_paid_200506 amount_paid_200505 amount_paid_200504 default  \n",
       "13126                  0                  0                  0       0  \n",
       "14636               2000               2000               2000       0  \n",
       "19430               7614              14053                  0       0  \n",
       "4382                7014                800                  0       0  \n",
       "7660                3000               5000                  0       0  \n",
       "10638                500                500               1000       0  \n",
       "17046               1000               1000               1000       1  \n",
       "24979               1009                596              10356       0  \n",
       "13640                547               3880                  0       1  \n",
       "9469                 300                928                464       1  \n",
       "18652               4500               5000               6000       0  \n",
       "12360                605               1000               2000       0  \n",
       "54                  1762               1790               1622       0  \n",
       "7013                5142               8766              15040       0  \n",
       "5223                1757              20000              10158       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for column_name in df.columns:\n",
    "    df[column_name] = pd.to_numeric(df[column_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit</th>\n",
       "      <th>sex</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>status_200509</th>\n",
       "      <th>status_200508</th>\n",
       "      <th>status_200507</th>\n",
       "      <th>status_200506</th>\n",
       "      <th>status_200505</th>\n",
       "      <th>status_200504</th>\n",
       "      <th>amount_charged_200509</th>\n",
       "      <th>amount_charged_200508</th>\n",
       "      <th>amount_charged_200507</th>\n",
       "      <th>amount_charged_200506</th>\n",
       "      <th>amount_charged_200505</th>\n",
       "      <th>amount_charged_200504</th>\n",
       "      <th>amount_paid_200509</th>\n",
       "      <th>amount_paid_200508</th>\n",
       "      <th>amount_paid_200507</th>\n",
       "      <th>amount_paid_200506</th>\n",
       "      <th>amount_paid_200505</th>\n",
       "      <th>amount_paid_200504</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>-0.291100</td>\n",
       "      <td>51223.330900</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>4.701315e+04</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>1.149988</td>\n",
       "      <td>73635.860576</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>6.934939e+04</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-1.572640e+05</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3558.750000</td>\n",
       "      <td>2984.750000</td>\n",
       "      <td>2.666250e+03</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22381.500000</td>\n",
       "      <td>21200.000000</td>\n",
       "      <td>2.008850e+04</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67091.000000</td>\n",
       "      <td>64006.250000</td>\n",
       "      <td>6.016475e+04</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>964511.000000</td>\n",
       "      <td>983931.000000</td>\n",
       "      <td>1.664089e+06</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                limit           sex     education      marriage           age  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "       status_200509  status_200508  status_200507  status_200506  \\\n",
       "count   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean       -0.016700      -0.133767      -0.166200      -0.220667   \n",
       "std         1.123802       1.197186       1.196868       1.169139   \n",
       "min        -2.000000      -2.000000      -2.000000      -2.000000   \n",
       "25%        -1.000000      -1.000000      -1.000000      -1.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         8.000000       8.000000       8.000000       8.000000   \n",
       "\n",
       "       status_200505  status_200504  amount_charged_200509  \\\n",
       "count   30000.000000   30000.000000           30000.000000   \n",
       "mean       -0.266200      -0.291100           51223.330900   \n",
       "std         1.133187       1.149988           73635.860576   \n",
       "min        -2.000000      -2.000000         -165580.000000   \n",
       "25%        -1.000000      -1.000000            3558.750000   \n",
       "50%         0.000000       0.000000           22381.500000   \n",
       "75%         0.000000       0.000000           67091.000000   \n",
       "max         8.000000       8.000000          964511.000000   \n",
       "\n",
       "       amount_charged_200508  amount_charged_200507  amount_charged_200506  \\\n",
       "count           30000.000000           3.000000e+04           30000.000000   \n",
       "mean            49179.075167           4.701315e+04           43262.948967   \n",
       "std             71173.768783           6.934939e+04           64332.856134   \n",
       "min            -69777.000000          -1.572640e+05         -170000.000000   \n",
       "25%              2984.750000           2.666250e+03            2326.750000   \n",
       "50%             21200.000000           2.008850e+04           19052.000000   \n",
       "75%             64006.250000           6.016475e+04           54506.000000   \n",
       "max            983931.000000           1.664089e+06          891586.000000   \n",
       "\n",
       "       amount_charged_200505  amount_charged_200504  amount_paid_200509  \\\n",
       "count           30000.000000           30000.000000        30000.000000   \n",
       "mean            40311.400967           38871.760400         5663.580500   \n",
       "std             60797.155770           59554.107537        16563.280354   \n",
       "min            -81334.000000         -339603.000000            0.000000   \n",
       "25%              1763.000000            1256.000000         1000.000000   \n",
       "50%             18104.500000           17071.000000         2100.000000   \n",
       "75%             50190.500000           49198.250000         5006.000000   \n",
       "max            927171.000000          961664.000000       873552.000000   \n",
       "\n",
       "       amount_paid_200508  amount_paid_200507  amount_paid_200506  \\\n",
       "count        3.000000e+04         30000.00000        30000.000000   \n",
       "mean         5.921163e+03          5225.68150         4826.076867   \n",
       "std          2.304087e+04         17606.96147        15666.159744   \n",
       "min          0.000000e+00             0.00000            0.000000   \n",
       "25%          8.330000e+02           390.00000          296.000000   \n",
       "50%          2.009000e+03          1800.00000         1500.000000   \n",
       "75%          5.000000e+03          4505.00000         4013.250000   \n",
       "max          1.684259e+06        896040.00000       621000.000000   \n",
       "\n",
       "       amount_paid_200505  amount_paid_200504       default  \n",
       "count        30000.000000        30000.000000  30000.000000  \n",
       "mean          4799.387633         5215.502567      0.221200  \n",
       "std          15278.305679        17777.465775      0.415062  \n",
       "min              0.000000            0.000000      0.000000  \n",
       "25%            252.500000          117.750000      0.000000  \n",
       "50%           1500.000000         1500.000000      0.000000  \n",
       "75%           4031.500000         4000.000000      0.000000  \n",
       "max         426529.000000       528666.000000      1.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22120000000000001"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## turn payment status into categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Author clarified codes for `payment_status*` columns\n",
    "> -2: No consumption; -1: Paid in full; 0: The use of revolving credit; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
    "\n",
    "So let's use these categories:\n",
    "\n",
    "- -2 => category 'no_consumption'\n",
    "- -1 => category 'paid_full'\n",
    "-  0 => category 'revolving'\n",
    "-  1 and 2 => 'delay_2_mths\n",
    "-  3 to 9  => 'delay_3+_mths'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fix_status(current_value):\n",
    "    if current_value == -2: return 'no_consumption'\n",
    "    elif current_value == -1: return 'paid_full'\n",
    "    elif current_value == 0: return 'revolving'\n",
    "    elif current_value in [1,2]: return 'delay_2_mths'\n",
    "    elif current_value in [3,4,5,6,7,8,9]: return 'delay_3+_mths'\n",
    "    else: return 'other'\n",
    "\n",
    "for column_name in df.columns:\n",
    "    if column_name.startswith('status'):\n",
    "        df[column_name] = df[column_name].map(lambda x: fix_status(x)).astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## one hot encoding where needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df,pd.get_dummies(df['sex'], prefix='sex')],axis=1)\n",
    "df.drop(['sex'],axis=1,inplace=True)\n",
    "\n",
    "df = pd.concat([df,pd.get_dummies(df['education'], prefix='education')],axis=1)\n",
    "df.drop(['education'],axis=1,inplace=True)\n",
    "\n",
    "df = pd.concat([df,pd.get_dummies(df['marriage'], prefix='marriage')],axis=1)\n",
    "df.drop(['marriage'],axis=1,inplace=True)\n",
    "\n",
    "# also all status columns\n",
    "for column_name in df.columns:\n",
    "    if column_name.startswith('status'):\n",
    "        df = pd.concat([df,pd.get_dummies(df[column_name], prefix=column_name)],axis=1)\n",
    "        df.drop([column_name],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit</th>\n",
       "      <th>age</th>\n",
       "      <th>amount_charged_200509</th>\n",
       "      <th>amount_charged_200508</th>\n",
       "      <th>amount_charged_200507</th>\n",
       "      <th>amount_charged_200506</th>\n",
       "      <th>amount_charged_200505</th>\n",
       "      <th>amount_charged_200504</th>\n",
       "      <th>amount_paid_200509</th>\n",
       "      <th>amount_paid_200508</th>\n",
       "      <th>amount_paid_200507</th>\n",
       "      <th>amount_paid_200506</th>\n",
       "      <th>amount_paid_200505</th>\n",
       "      <th>amount_paid_200504</th>\n",
       "      <th>default</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>sex_2</th>\n",
       "      <th>education_0</th>\n",
       "      <th>education_1</th>\n",
       "      <th>education_2</th>\n",
       "      <th>education_3</th>\n",
       "      <th>education_4</th>\n",
       "      <th>education_5</th>\n",
       "      <th>education_6</th>\n",
       "      <th>marriage_0</th>\n",
       "      <th>marriage_1</th>\n",
       "      <th>marriage_2</th>\n",
       "      <th>marriage_3</th>\n",
       "      <th>status_200509_delay_2_mths</th>\n",
       "      <th>status_200509_delay_3+_mths</th>\n",
       "      <th>status_200509_no_consumption</th>\n",
       "      <th>status_200509_paid_full</th>\n",
       "      <th>status_200509_revolving</th>\n",
       "      <th>status_200508_delay_2_mths</th>\n",
       "      <th>status_200508_delay_3+_mths</th>\n",
       "      <th>status_200508_no_consumption</th>\n",
       "      <th>status_200508_paid_full</th>\n",
       "      <th>status_200508_revolving</th>\n",
       "      <th>status_200507_delay_2_mths</th>\n",
       "      <th>status_200507_delay_3+_mths</th>\n",
       "      <th>status_200507_no_consumption</th>\n",
       "      <th>status_200507_paid_full</th>\n",
       "      <th>status_200507_revolving</th>\n",
       "      <th>status_200506_delay_2_mths</th>\n",
       "      <th>status_200506_delay_3+_mths</th>\n",
       "      <th>status_200506_no_consumption</th>\n",
       "      <th>status_200506_paid_full</th>\n",
       "      <th>status_200506_revolving</th>\n",
       "      <th>status_200505_delay_2_mths</th>\n",
       "      <th>status_200505_delay_3+_mths</th>\n",
       "      <th>status_200505_no_consumption</th>\n",
       "      <th>status_200505_paid_full</th>\n",
       "      <th>status_200505_revolving</th>\n",
       "      <th>status_200504_delay_2_mths</th>\n",
       "      <th>status_200504_delay_3+_mths</th>\n",
       "      <th>status_200504_no_consumption</th>\n",
       "      <th>status_200504_paid_full</th>\n",
       "      <th>status_200504_revolving</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28088</th>\n",
       "      <td>120000</td>\n",
       "      <td>33</td>\n",
       "      <td>508</td>\n",
       "      <td>607</td>\n",
       "      <td>611</td>\n",
       "      <td>593</td>\n",
       "      <td>653</td>\n",
       "      <td>587</td>\n",
       "      <td>700</td>\n",
       "      <td>611</td>\n",
       "      <td>593</td>\n",
       "      <td>653</td>\n",
       "      <td>587</td>\n",
       "      <td>916</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>240000</td>\n",
       "      <td>30</td>\n",
       "      <td>7818</td>\n",
       "      <td>9363</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9434</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25412</th>\n",
       "      <td>30000</td>\n",
       "      <td>24</td>\n",
       "      <td>25616</td>\n",
       "      <td>27526</td>\n",
       "      <td>11458</td>\n",
       "      <td>7500</td>\n",
       "      <td>7000</td>\n",
       "      <td>5192</td>\n",
       "      <td>31348</td>\n",
       "      <td>1328</td>\n",
       "      <td>1000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19795</th>\n",
       "      <td>180000</td>\n",
       "      <td>39</td>\n",
       "      <td>2569</td>\n",
       "      <td>11662</td>\n",
       "      <td>11867</td>\n",
       "      <td>8111</td>\n",
       "      <td>6312</td>\n",
       "      <td>3739</td>\n",
       "      <td>11662</td>\n",
       "      <td>6003</td>\n",
       "      <td>8111</td>\n",
       "      <td>273</td>\n",
       "      <td>3739</td>\n",
       "      <td>6547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17523</th>\n",
       "      <td>120000</td>\n",
       "      <td>44</td>\n",
       "      <td>16735</td>\n",
       "      <td>18150</td>\n",
       "      <td>19528</td>\n",
       "      <td>19065</td>\n",
       "      <td>18617</td>\n",
       "      <td>21381</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17800</th>\n",
       "      <td>50000</td>\n",
       "      <td>23</td>\n",
       "      <td>18666</td>\n",
       "      <td>15554</td>\n",
       "      <td>17159</td>\n",
       "      <td>8402</td>\n",
       "      <td>6646</td>\n",
       "      <td>7534</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1500</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13470</th>\n",
       "      <td>240000</td>\n",
       "      <td>46</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2240</td>\n",
       "      <td>1681</td>\n",
       "      <td>2267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2240</td>\n",
       "      <td>0</td>\n",
       "      <td>2267</td>\n",
       "      <td>3074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>50000</td>\n",
       "      <td>41</td>\n",
       "      <td>26184</td>\n",
       "      <td>29261</td>\n",
       "      <td>28444</td>\n",
       "      <td>28878</td>\n",
       "      <td>27655</td>\n",
       "      <td>24480</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22442</th>\n",
       "      <td>330000</td>\n",
       "      <td>36</td>\n",
       "      <td>257156</td>\n",
       "      <td>257576</td>\n",
       "      <td>256014</td>\n",
       "      <td>245244</td>\n",
       "      <td>239441</td>\n",
       "      <td>242025</td>\n",
       "      <td>10037</td>\n",
       "      <td>10109</td>\n",
       "      <td>10053</td>\n",
       "      <td>10014</td>\n",
       "      <td>9001</td>\n",
       "      <td>9001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18143</th>\n",
       "      <td>300000</td>\n",
       "      <td>32</td>\n",
       "      <td>8077</td>\n",
       "      <td>10566</td>\n",
       "      <td>11293</td>\n",
       "      <td>12000</td>\n",
       "      <td>3458</td>\n",
       "      <td>56338</td>\n",
       "      <td>2800</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>9427</td>\n",
       "      <td>55000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        limit  age  amount_charged_200509  amount_charged_200508  \\\n",
       "28088  120000   33                    508                    607   \n",
       "1361   240000   30                   7818                   9363   \n",
       "25412   30000   24                  25616                  27526   \n",
       "19795  180000   39                   2569                  11662   \n",
       "17523  120000   44                  16735                  18150   \n",
       "17800   50000   23                  18666                  15554   \n",
       "13470  240000   46                    456                      0   \n",
       "2660    50000   41                  26184                  29261   \n",
       "22442  330000   36                 257156                 257576   \n",
       "18143  300000   32                   8077                  10566   \n",
       "\n",
       "       amount_charged_200507  amount_charged_200506  amount_charged_200505  \\\n",
       "28088                    611                    593                    653   \n",
       "1361                       0                      0                      0   \n",
       "25412                  11458                   7500                   7000   \n",
       "19795                  11867                   8111                   6312   \n",
       "17523                  19528                  19065                  18617   \n",
       "17800                  17159                   8402                   6646   \n",
       "13470                      0                   2240                   1681   \n",
       "2660                   28444                  28878                  27655   \n",
       "22442                 256014                 245244                 239441   \n",
       "18143                  11293                  12000                   3458   \n",
       "\n",
       "       amount_charged_200504  amount_paid_200509  amount_paid_200508  \\\n",
       "28088                    587                 700                 611   \n",
       "1361                       0                9434                   0   \n",
       "25412                   5192               31348                1328   \n",
       "19795                   3739               11662                6003   \n",
       "17523                  21381                2000                2000   \n",
       "17800                   7534                2000                2000   \n",
       "13470                   2267                   0                   0   \n",
       "2660                   24480                3500                   0   \n",
       "22442                 242025               10037               10109   \n",
       "18143                  56338                2800                1200   \n",
       "\n",
       "       amount_paid_200507  amount_paid_200506  amount_paid_200505  \\\n",
       "28088                 593                 653                 587   \n",
       "1361                    0                   0                   0   \n",
       "25412                1000                7000                5192   \n",
       "19795                8111                 273                3739   \n",
       "17523                2000                1000                3000   \n",
       "17800                1500                 500                1000   \n",
       "13470                2240                   0                2267   \n",
       "2660                 1100                1100                2000   \n",
       "22442               10053               10014                9001   \n",
       "18143                1200                9427               55000   \n",
       "\n",
       "       amount_paid_200504  default  sex_1  sex_2  education_0  education_1  \\\n",
       "28088                 916        0      0      1            0            0   \n",
       "1361                  178        0      0      1            0            0   \n",
       "25412                   0        0      0      1            0            1   \n",
       "19795                6547        0      0      1            0            0   \n",
       "17523                2000        0      0      1            0            0   \n",
       "17800                2000        0      1      0            0            1   \n",
       "13470                3074        0      0      1            0            1   \n",
       "2660                 1000        0      1      0            0            0   \n",
       "22442                9001        0      0      1            0            0   \n",
       "18143                2000        1      1      0            0            1   \n",
       "\n",
       "       education_2  education_3  education_4  education_5  education_6  \\\n",
       "28088            1            0            0            0            0   \n",
       "1361             1            0            0            0            0   \n",
       "25412            0            0            0            0            0   \n",
       "19795            1            0            0            0            0   \n",
       "17523            0            1            0            0            0   \n",
       "17800            0            0            0            0            0   \n",
       "13470            0            0            0            0            0   \n",
       "2660             0            1            0            0            0   \n",
       "22442            1            0            0            0            0   \n",
       "18143            0            0            0            0            0   \n",
       "\n",
       "       marriage_0  marriage_1  marriage_2  marriage_3  \\\n",
       "28088           0           0           1           0   \n",
       "1361            0           0           1           0   \n",
       "25412           0           0           1           0   \n",
       "19795           0           1           0           0   \n",
       "17523           0           1           0           0   \n",
       "17800           0           0           1           0   \n",
       "13470           0           0           1           0   \n",
       "2660            0           1           0           0   \n",
       "22442           0           0           1           0   \n",
       "18143           0           0           1           0   \n",
       "\n",
       "       status_200509_delay_2_mths  status_200509_delay_3+_mths  \\\n",
       "28088                           0                            0   \n",
       "1361                            0                            0   \n",
       "25412                           0                            0   \n",
       "19795                           0                            0   \n",
       "17523                           0                            0   \n",
       "17800                           0                            0   \n",
       "13470                           1                            0   \n",
       "2660                            1                            0   \n",
       "22442                           0                            0   \n",
       "18143                           0                            0   \n",
       "\n",
       "       status_200509_no_consumption  status_200509_paid_full  \\\n",
       "28088                             0                        1   \n",
       "1361                              0                        0   \n",
       "25412                             0                        1   \n",
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       "\n",
       "       status_200509_revolving  status_200508_delay_2_mths  \\\n",
       "28088                        0                           0   \n",
       "1361                         1                           0   \n",
       "25412                        0                           0   \n",
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       "17523                        1                           0   \n",
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       "2660                         0                           1   \n",
       "22442                        1                           0   \n",
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       "\n",
       "       status_200508_delay_3+_mths  status_200508_no_consumption  \\\n",
       "28088                            0                             0   \n",
       "1361                             0                             0   \n",
       "25412                            0                             0   \n",
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       "2660                             0                             0   \n",
       "22442                            0                             0   \n",
       "18143                            0                             0   \n",
       "\n",
       "       status_200508_paid_full  status_200508_revolving  \\\n",
       "28088                        1                        0   \n",
       "1361                         1                        0   \n",
       "25412                        1                        0   \n",
       "19795                        1                        0   \n",
       "17523                        0                        1   \n",
       "17800                        0                        1   \n",
       "13470                        0                        0   \n",
       "2660                         0                        0   \n",
       "22442                        0                        1   \n",
       "18143                        0                        1   \n",
       "\n",
       "       status_200507_delay_2_mths  status_200507_delay_3+_mths  \\\n",
       "28088                           0                            0   \n",
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       "2660                            1                            0   \n",
       "22442                           0                            0   \n",
       "18143                           1                            0   \n",
       "\n",
       "       status_200507_no_consumption  status_200507_paid_full  \\\n",
       "28088                             0                        1   \n",
       "1361                              0                        1   \n",
       "25412                             0                        0   \n",
       "19795                             0                        0   \n",
       "17523                             0                        0   \n",
       "17800                             0                        0   \n",
       "13470                             1                        0   \n",
       "2660                              0                        0   \n",
       "22442                             0                        0   \n",
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       "\n",
       "       status_200507_revolving  status_200506_delay_2_mths  \\\n",
       "28088                        0                           0   \n",
       "1361                         0                           0   \n",
       "25412                        1                           0   \n",
       "19795                        1                           0   \n",
       "17523                        1                           0   \n",
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       "2660                         0                           0   \n",
       "22442                        1                           0   \n",
       "18143                        0                           1   \n",
       "\n",
       "       status_200506_delay_3+_mths  status_200506_no_consumption  \\\n",
       "28088                            0                             0   \n",
       "1361                             0                             1   \n",
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       "\n",
       "       status_200506_paid_full  status_200506_revolving  \\\n",
       "28088                        1                        0   \n",
       "1361                         0                        0   \n",
       "25412                        0                        1   \n",
       "19795                        1                        0   \n",
       "17523                        0                        1   \n",
       "17800                        0                        1   \n",
       "13470                        1                        0   \n",
       "2660                         0                        1   \n",
       "22442                        0                        1   \n",
       "18143                        0                        0   \n",
       "\n",
       "       status_200505_delay_2_mths  status_200505_delay_3+_mths  \\\n",
       "28088                           0                            0   \n",
       "1361                            0                            0   \n",
       "25412                           0                            0   \n",
       "19795                           0                            0   \n",
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       "17800                           0                            0   \n",
       "13470                           0                            0   \n",
       "2660                            0                            0   \n",
       "22442                           0                            0   \n",
       "18143                           0                            0   \n",
       "\n",
       "       status_200505_no_consumption  status_200505_paid_full  \\\n",
       "28088                             0                        1   \n",
       "1361                              1                        0   \n",
       "25412                             0                        0   \n",
       "19795                             0                        0   \n",
       "17523                             0                        0   \n",
       "17800                             0                        0   \n",
       "13470                             0                        0   \n",
       "2660                              0                        0   \n",
       "22442                             0                        0   \n",
       "18143                             0                        1   \n",
       "\n",
       "       status_200505_revolving  status_200504_delay_2_mths  \\\n",
       "28088                        0                           0   \n",
       "1361                         0                           0   \n",
       "25412                        1                           0   \n",
       "19795                        1                           0   \n",
       "17523                        1                           0   \n",
       "17800                        1                           0   \n",
       "13470                        1                           0   \n",
       "2660                         1                           0   \n",
       "22442                        1                           0   \n",
       "18143                        0                           0   \n",
       "\n",
       "       status_200504_delay_3+_mths  status_200504_no_consumption  \\\n",
       "28088                            0                             0   \n",
       "1361                             0                             1   \n",
       "25412                            0                             0   \n",
       "19795                            0                             0   \n",
       "17523                            0                             0   \n",
       "17800                            0                             0   \n",
       "13470                            0                             0   \n",
       "2660                             0                             0   \n",
       "22442                            0                             0   \n",
       "18143                            0                             0   \n",
       "\n",
       "       status_200504_paid_full  status_200504_revolving  \n",
       "28088                        1                        0  \n",
       "1361                         0                        0  \n",
       "25412                        1                        0  \n",
       "19795                        0                        1  \n",
       "17523                        0                        1  \n",
       "17800                        0                        1  \n",
       "13470                        1                        0  \n",
       "2660                         0                        1  \n",
       "22442                        0                        1  \n",
       "18143                        0                        1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop('default',axis=1)\n",
    "target = df['default']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    limit  age  amount_charged_200509  amount_charged_200508  \\\n",
       "1   20000   24                   3913                   3102   \n",
       "2  120000   26                   2682                   1725   \n",
       "3   90000   34                  29239                  14027   \n",
       "4   50000   37                  46990                  48233   \n",
       "5   50000   57                   8617                   5670   \n",
       "\n",
       "   amount_charged_200507  amount_charged_200506  amount_charged_200505  \\\n",
       "1                    689                      0                      0   \n",
       "2                   2682                   3272                   3455   \n",
       "3                  13559                  14331                  14948   \n",
       "4                  49291                  28314                  28959   \n",
       "5                  35835                  20940                  19146   \n",
       "\n",
       "   amount_charged_200504  amount_paid_200509  amount_paid_200508  \\\n",
       "1                      0                   0                 689   \n",
       "2                   3261                   0                1000   \n",
       "3                  15549                1518                1500   \n",
       "4                  29547                2000                2019   \n",
       "5                  19131                2000               36681   \n",
       "\n",
       "   amount_paid_200507  amount_paid_200506  amount_paid_200505  \\\n",
       "1                   0                   0                   0   \n",
       "2                1000                1000                   0   \n",
       "3                1000                1000                1000   \n",
       "4                1200                1100                1069   \n",
       "5               10000                9000                 689   \n",
       "\n",
       "   amount_paid_200504  sex_1  sex_2  education_0  education_1  education_2  \\\n",
       "1                   0      0      1            0            0            1   \n",
       "2                2000      0      1            0            0            1   \n",
       "3                5000      0      1            0            0            1   \n",
       "4                1000      0      1            0            0            1   \n",
       "5                 679      1      0            0            0            1   \n",
       "\n",
       "   education_3  education_4  education_5  education_6  marriage_0  marriage_1  \\\n",
       "1            0            0            0            0           0           1   \n",
       "2            0            0            0            0           0           0   \n",
       "3            0            0            0            0           0           0   \n",
       "4            0            0            0            0           0           1   \n",
       "5            0            0            0            0           0           1   \n",
       "\n",
       "   marriage_2  marriage_3  status_200509_delay_2_mths  \\\n",
       "1           0           0                           1   \n",
       "2           1           0                           0   \n",
       "3           1           0                           0   \n",
       "4           0           0                           0   \n",
       "5           0           0                           0   \n",
       "\n",
       "   status_200509_delay_3+_mths  status_200509_no_consumption  \\\n",
       "1                            0                             0   \n",
       "2                            0                             0   \n",
       "3                            0                             0   \n",
       "4                            0                             0   \n",
       "5                            0                             0   \n",
       "\n",
       "   status_200509_paid_full  status_200509_revolving  \\\n",
       "1                        0                        0   \n",
       "2                        1                        0   \n",
       "3                        0                        1   \n",
       "4                        0                        1   \n",
       "5                        1                        0   \n",
       "\n",
       "   status_200508_delay_2_mths  status_200508_delay_3+_mths  \\\n",
       "1                           1                            0   \n",
       "2                           1                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "5                           0                            0   \n",
       "\n",
       "   status_200508_no_consumption  status_200508_paid_full  \\\n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "5                             0                        0   \n",
       "\n",
       "   status_200508_revolving  status_200507_delay_2_mths  \\\n",
       "1                        0                           0   \n",
       "2                        0                           0   \n",
       "3                        1                           0   \n",
       "4                        1                           0   \n",
       "5                        1                           0   \n",
       "\n",
       "   status_200507_delay_3+_mths  status_200507_no_consumption  \\\n",
       "1                            0                             0   \n",
       "2                            0                             0   \n",
       "3                            0                             0   \n",
       "4                            0                             0   \n",
       "5                            0                             0   \n",
       "\n",
       "   status_200507_paid_full  status_200507_revolving  \\\n",
       "1                        1                        0   \n",
       "2                        0                        1   \n",
       "3                        0                        1   \n",
       "4                        0                        1   \n",
       "5                        1                        0   \n",
       "\n",
       "   status_200506_delay_2_mths  status_200506_delay_3+_mths  \\\n",
       "1                           0                            0   \n",
       "2                           0                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "5                           0                            0   \n",
       "\n",
       "   status_200506_no_consumption  status_200506_paid_full  \\\n",
       "1                             0                        1   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "5                             0                        0   \n",
       "\n",
       "   status_200506_revolving  status_200505_delay_2_mths  \\\n",
       "1                        0                           0   \n",
       "2                        1                           0   \n",
       "3                        1                           0   \n",
       "4                        1                           0   \n",
       "5                        1                           0   \n",
       "\n",
       "   status_200505_delay_3+_mths  status_200505_no_consumption  \\\n",
       "1                            0                             1   \n",
       "2                            0                             0   \n",
       "3                            0                             0   \n",
       "4                            0                             0   \n",
       "5                            0                             0   \n",
       "\n",
       "   status_200505_paid_full  status_200505_revolving  \\\n",
       "1                        0                        0   \n",
       "2                        0                        1   \n",
       "3                        0                        1   \n",
       "4                        0                        1   \n",
       "5                        0                        1   \n",
       "\n",
       "   status_200504_delay_2_mths  status_200504_delay_3+_mths  \\\n",
       "1                           0                            0   \n",
       "2                           1                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "5                           0                            0   \n",
       "\n",
       "   status_200504_no_consumption  status_200504_paid_full  \\\n",
       "1                             1                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "5                             0                        0   \n",
       "\n",
       "   status_200504_revolving  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        1  \n",
       "4                        1  \n",
       "5                        1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hp7AxhXm4PRSa4/ZaPigZ+COYQVVmf2zazWeLtgLw011nERZm3zLLtdRU+PJL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\n/wfGqiI0e2jLLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e627a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features=43\n",
    "\n",
    "plt.clf()\n",
    "d = dict(zip(data.columns, clf.feature_importances_))\n",
    "\n",
    "d['marriage'] = 0\n",
    "d['sex'] = 0\n",
    "d['education'] = 0\n",
    "\n",
    "for n in range(0,3):\n",
    "    d['marriage'] += d['marriage_{}'.format(n)]\n",
    "    del(d['marriage_{}'.format(n)])\n",
    "\n",
    "for n in range(1,3):\n",
    "    d['sex'] += d['sex_{}'.format(n)]\n",
    "    del(d['sex_{}'.format(n)])    \n",
    "    \n",
    "for n in range(0,7):\n",
    "    d['education'] += d['education_{}'.format(n)]\n",
    "    del(d['education_{}'.format(n)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TbR3kmuqk95W79/KgW9533X1r+PlH7n6Ou5/r7le5+8fh5/vcvb27t3X389z9F5VtjsuT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\ns1Yc1SmNeO4VY9BAOCF7uaRcVX0biyiRKzcGaeWq6q3ArSKyZcxgNvR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yuEcffd0PxVRg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w4REeB5VV3TQ+dU9bEGf1M6DmSe9uHFcAPtHG2rEES2wWCd21FuDIrWObYdpUWXHCCLxdlTLKj5\nRsAY4IvKeVXdKWf541V1o1ZzY9BOcmOeb1HcRBltUzcibTUCWyH9LywUz8dYx3YU9jqukQ9kFnZe\nCjgR2NaV9RBwcr1OWDpmefoT0A8bRO0C9FOLx5w5ROR1zCf0W5itRgJ3qOpMD+45mA/ekYkZmemq\nmos/XkLuJHULQ8UyYl2iqjeLyHbAqZpzpjURWRU4Gxv0KRbe8PAms8Bz3T1E5N+qunKtc3W4E4H9\nVfXJquODgEs9Z4FT1cca/O7YoCL55ujfeXID7ZyFrX4InIkt5BK35fmHL1rndpKbkJPazp1A56h2\nlFpeFx0gf6PReVV9MGf5vwNGq/OTaRU3Bu0kN+b5FsVNlNHyuiHhPmMxtpqoqhsk9h9X1S3EolGM\nV9V185DrC6mK0Vs1gBqP+W3OcjMjE5L3Uqe8UBs/raobi0gfbGZlLywu6T+Akap6TxP+plgYrluA\n84AXVXVAI04sJOEvKCJPaiLNa/VzLwIicoyqnl51bCxm277AXcBgVX3KzYCPaqSziGyCxWldnHmv\nlVcCPsJmVMdmoHPdmNEicgg2KHgbexNTGcg0tXMM16PsWnaOtpWIvAjsqKpTUuoT2gajdG43uQl+\najsXrbOHfpnGXu+SA+QKRGQ14E2dt0BhYSx+38s5y50BLIrNes0ixT/gGG4M2lFuzPMtkNvSuiHZ\n+E+mvl83IBmqqi+5TvUsVd3WnZusqgPzkOsLqQrnJSLTgP+HreL+XXIALzn6XVbr4Y71wxJWDNUG\nr/4T13fDYoIOAVZX1RWacWIgFqB/ReBkYM//3965R19SVXf+s1toOkzo1g5jNww9IspjQCHBaWWy\nWAGCiihLArTomnHGxoG44gvjTAKKwopKAsQYEAQDRsBxLUwEDQwNbUvLYyABoRts5O0CfIbgA6Rd\n0r7Y88ep27/iUvfeqrPP49b9ne9aZ/3urXt37W99z6m6+1e1zz64lI4v4+6CH6OqR8T0PwkjND0U\nOB+36tYJwJ/i6gMvBk5Q1Stb7Hc5tR92rWbix+Jc++xbuEoUP/bYr7dti32P4+ytlYjc0vUpRKDr\nXGfOPffbWefcnFv4CFtaUhMUds7VgDuAhbX3C3EzeLNzKy1v/+ayzaDRgyO2C+6iFOV4ccHSd4Bv\n4QrQv6ra/u+Bs3LrzNAiFrh0inpbVm1fDqyPpTFwU8C+3gl4faJxtRq4DfgRrtzgvcBfAktS+J/A\nrdXCKMCOwPM67Hfbpn0E4jxuwZzrgW089+tta9HZohUureMfcHf8jx60CTYhrnOdOffcb2edc3Nu\nwSvo4kSzXgd5G63l8qnqL0VkYWynIvIHTdtV9aaYthb01K+lf7PYZhgbW0RkpQ7ljOEe42+Z5LNC\n5+NV1a+JyItw+bs/qm3/IW4lpih+faGqx43Y/hhw6ARzb421uqvuC3GLyBxJ7W6OuDzkTo+nu0Jd\ndYpLYvowoPGxaJNWuEUK7h+3MxE5BFfqbJGIbAT+WOeeYqwDYi+G8jBwg7h873ou/ici207Cc3QO\npNVi3FOJel1cZW6xkyZ4n4NGzr3zW0NnnaeAc1LMeoD8QxF5o6peBSAiR+LueMRGfbWzRbjZ8Btw\nd9Vi2lrQR7+W/s1lm3psrAYuEJGmnLHVrRj7H++ewJEiUg9IruoQvMU8f5+zIMSIAKoN39UYNPb1\nKyIn4e7+fAE3kRHcI8vLROQLqnrGJN++MGiVAk19O0qrL7TQ6izgMFW9R0RWAV8Vkf+uqrc2+QrF\nuYbvVG1h1brAYjsJTZzNWo36Z3Wr04bcZ2znoIVzH/0C3jpn5dwCYRdFCnk7etoabpGBW5m7SPwz\nLkcvNY8VwBWpbXNxTuXX0r+5bHONDVyqwCuqtryjn87Hi6tWcReurM9bq3byYFtqnRv2vToC384a\nW/ziyuY1PepcSKBHlrH6NmYDPhhSK9wkzfr7fXAl7f6IQI90h8djR9tzM9k26ZxCq3HpKD7noJlz\nn/xadO4B59VB9xdyZ9PagN8Gfrth+9sS+Rfg3tS2uTin9mvp31y2qcfGiOCgU/5kl+O1BCRGvwuA\ntwNrgG8AG3F3DQ+e4MPM10dji19casCLGra/CHjAZ0y1PM4sgfkoLi2/560VLhd++dC2XXD/EGye\nYLtvfXzgFiu5CpevvX0gDbwDzyZbYDdcbeuPVefeRbh64F8Edo2lVQfOQXOfQ3Duk1+Lzhm1evfg\nO8BLceXdnsTNg3h5iONtarOeYgGAji7AfyJwaWh/InIuc/lZC3C1XDfGtrWgz34t/ZvaNvXYCJk/\n2fF4nwF2xtVCrmOn6rPW6Oj37yuff8Wz6y9/SERerqPrL3vzNWps0el9wHoReQj4brXtP+J+QN49\nwdaCYH3bBTJiYZTBdh1fCcai1cm4OsJbZ+qr6vfElSOcZHsJc/1/Bq6+9t/g7rh9GjeDf9pwCXOL\nqtyKm7T6EVyu6mcZn9Jl0aotQuc+e3Puqd+2aMrlz8X5T1T1vOr1OcDf6lzt9U8ztxBOUMyLAHkM\nwuarzOGO2utf4+qZ3pLA1oJZ9Gvp31i2qcdGrvzJFMFbk99X6Fxu3c3i6i+fKm4ltbuAUQGyha9F\nY2+/qrpWRPbA5aLXc4FvV9XfjLY0I1dgfjGeC6NYtFLV60Zs/ylw+uC9iFyhqscMfa3e/4cyV1/7\nJtwTjmnEDqp6AYCIvFNV/6ba/vfiVlEcCaNWbRE099nIuXd+O+A5+8nIuR6rvlCrJbFV9YYqpzkK\n5nuA3PQfkn2nqpdWs+0HSzI+kMLWghn1a+nfKLYZxsZCVb2nsr9cRO4DvlRNWgo1/p+zn0TBWxP/\nX4nIS3Su/vIvKz6/EJFx/WLh662xVSdVfQZ3lw8AEVkaOTjOFpir6nvFLYxymYgMFkZpPYYTaNW0\nQMsSETkK98RnO1X9VcVFx43Hjgj9z/wzVf8uwd2h/886t6jK8wy+6rAsZvPFhm0prnNNnGfZb5PO\nbRGa8+UicgnuScaXReR9zNVeb7WipBdi5W70odGyXqbHfg/GPX68EZcr8whuvfCotrk4T6tfS//G\nsk09NsicEzj0vaWBx05Tjpy5/nJXvqE17uD3Q7XXe+Nygx8BHh0cd6oWum8n+FoAvBeXOvODadGK\n5nzei/Gsr93B7+qQtrg73Q8A9wEHAldU59PjwJGxtBrxvbY55imuc0392zu/GHLMc2pFhtrrUXY6\nLQ148bhtwHmR/G4A9qy93wPYENs2F+eMWnn3b0bbpGMDeDWwX8P2JcApsXQmQEDiqzPurljXCYje\nfC0aG/1urL1eAxxevX4lbkny1sefSqvAPFovjJJCKwIvUoCrR/tWGiaotrANOqmJjouq+GiFC3qe\nqtrmqv1msH3C/szXOU/OvfNbjYU/weUTfxO3gugK4H8CX5tGzrladgJRD665k1IEfZvabAttm4tz\nRq28+zej7VSODcaUi/M53hABia/OwF64UmSfrNpJwH+KzddHY4vfIds7hz6L8nQslVYh+zaVVqP2\nY+D8feBy4CfAPwJHUVtZcoLtPUN9dFT1+mDgFk+d9wrYj01Pfz6JW4Z4WW3bI4HHj3fpUss4mSa/\n9f0B3wnlK7ZWvueRpc1kDnJVxH4fXP7X0bWPFuMWWIiNO0TkM8Dnq/f/jWdPsIpla0Fv/Fr6N5dt\nDdM6Np6TMxbwPNpZVa8FUNWvi8hvjfuysY9CLJ7RiW8HTMq77Op3NxG5CnfHfBcR2V5Vf159tq2R\na1vE0uo5MPZtUK1E5AXAClXdVNt8UmDOj6vqKhFZjFuU5QTgQhG5GjdBd90YW+9JTWM4t1lUpWl/\nrbRSY455S7TKfW7Luad+g+aYp+Ac6LreGVJF5jMFcStu/RHwRlzNyQE2A19Q1X+O7H874F24/C1w\nuXLnq+ovRlvZbS3ok19L/+ayre1jKseGiGxU1f2Htlm0ehL3KE+AA3A1aH9effZNVX3ZGFuL3weB\nfbSaDFXbvhB3V2330HzbYoTGFp0OGtq0UVU3i8gyYJWqfsrKeYTf6FqN8OvVt9V3zFqJyA24MbkN\nLt3pcdzd2PdH4tw0Xn4HeBNwrKqOLLcmIqfjJlB+BHgLbknhwaSmY1T1iBica9+9gY5a1WwX4FJE\n3oRbGGjnSTZt0aRpCM598isihwLn40oyngD8KbAf7gbECap6ZYv9puZsHpNeiHl7OncD/ktuDqVN\nZ//msp3WxvjVqTofL3DQUNuh2r4MeFcsnfFcECIEXx+NU/iNMFaycPbt24D+76z+Hg/8RfV6bIqT\nhTNwk5HvajwmNYXQ2Uerhn20zjHvsM9x1zkz5z75Hfpepxzz1JxznfszmWJRw7dE5IPArtQeOanq\n22M6FZEjgI/iOm8b3J0W1fGF7M22FvTUr6V/s9hO8dgYVyqq8/Gq6o0jtv8b0PbOpo/OXjV6A/Gd\nhKa6ot5+RWQJ8AHc3fYX4h5HPw5cCZyhqk9aCY/glkKrJnjXXw6k1TYishNwLHBKbM6q+gctfYyy\nvwS36EdXhKhz7aPVIL3qSGrlA8XVur6v7T4muRjzmRfnPvpt0hl3Ltzfcr+pOWepvT7rAfKVuMfQ\n1+Fmw6bC2cDRwN1a/ZuTyNaCPvq19G8u2+xjwyNnrPPxBgpIOvtVzxq9oYPNDjmqFr//CHwNt4z2\nY9X+lgNvqz57bRfObZExMLfUXw6h1UeArwA3q+rtIrIb8FBEzqMCmavaBIy+tlbOFTprFTrP1OM6\n15lzH/0GyjFPyjnQmOyOWLemp6EBd2Xyez2wILVtLs4ZtfLu34y2WcYGcAMux2wpriTXbcAnYh0v\n7uJ5ErW6l7jarycB62LrPLSfiTV6A/HtrLHFL+NTRqI9dgyhVUAubWtGZ9HKyPkkXI3Yk3Hl3t5a\nvb4LODmWrYWzUZcHgW0bti8EHmq5D+/rnJF7b/yG0HkatEoxJmdykt4AIvIxXNmhaxL7XYl7FH4j\nsHUClap+IqatBX30a+nfjLZZxoaI3Kmqvycix+P+Yz9NRDap6r4tbDsfr4g8oKp7dv0sgN8PqerH\nqtd7A/+Eq1IgwJtV9baIfDtrbPErIutwd9cv1bmll5fhck9fo6qvnsTZByG08vTr1bfV981aicjF\nNK8aOTLlx8jZMsHPYuvNubYPH63uxy1F/O2h7S/C/eMV5Ry0cO6j30A6p+ZsHpM+WBBjp1OEE4Gr\nReRpEXlKRDaLyFMJ/J6OmzW8CNih1mLbWtBHv5b+zWWba2zUc8aubmkzgM/xfltE/rwKQgAXkFSP\n9747xs7qt14W7q+BE1X1xbjj/tvIfH00tvh9M/A7wI0i8oSIPIG7O7O04hALIbTygW/fQhitrsbV\nFF4DrMfdBftZRM7PAE0VHHaqPotla+E8gI9WgzzTa0XkwqqtrexPbOnXcp3z4dxHvyF0Ts05xJjs\njti3qOdjA76ZwzYX5z76zdVyjQ1cyaRNuLJw4GpNehevb+HvBcCZuEkfT1TtvmpbtEdjeC4IEYKv\nj8a5dJpPfRuRzwIiLuYCvA63xPO1wIVVW1tte11E2+A6t9Gq9r0DgGOqdgDdqisEu8615dxHv1ad\nU3POde7PeopF4yxgVb0pst+zgOt0fCH34LYW9NGvpX8z2vZxbOQ6jzr7lUw1enNBRA7DTZZ71mx0\nVV2bj1UcWPs2tFYisiewRlVfGpHzAvwn+HnZxjiH2mg1wm6pqv6kq78Q8OXcR79WnWNzznVdn/UA\n+f/W3i7CXSw26JgC64H8bgb+HS5P9FfQqZSXt60FffRr6d+MtlnGhjFnzOt4rQGJj18xLAgRgK+X\nxr5+ReRsYA/c8rzfqzbvAvwP3GSbto9LOyNHYG7sW7NW1flX79/HgA+o6hUxOI/Yn3cg09Y2BGdP\nrbLkPls499FvIJ1Tcw56HrXFTAfIwxCRFcDZqnpMAl9Lgd2pLY2rI+qHhrS1oO9+Lf2b0jbH2BCR\nOrdFwFHAD1T1vW38Du1r4vHGCN5inr+BAqjOGlv8isiDqrpHw3YBHtRIq0vlDMx9kUsrCyyBTIgg\nKDWktoKaiKwBzlPVa0Xklbjz/vdb7CPYda4L+uQ3hM6pOWeDRsrdmMaGuzjcm8DP8cDduNy864Gn\ngSa02ugAABQwSURBVPWxbXNxnha/lv5NZTstY4MOOWM+x4sLOkbZepUSaul3CXAGLj/2J8CPcfmx\nZwDPT8y3TY6qt19cHt/Khu2vxNXK7sy55XEF16qlX6++DaVV07k26fwzcq7nXa4BDq9x7pL73NXW\nm7NRq2y5z76c++g3hM4ZOJvHpE+b6YVCRORc5m7lLwB+F9iYwPWJwErgVlU9RFzB9r9MYGtB7/xa\n+jeXLdMzNnbHLfIwEZ7Hu0VEVqrq7UPbVwJbIvr1XRDCzLcBbTS2+F0NXCAiOzB3J3cF8NPqs1iI\noVUbWBb7WI2nViKyCNge2FHcwgaDlb4WM5diEoNzHTur6rUAqvp1EfmtlnY+tt6cjVrtJiJXVTa7\niMj2WuWZ4u5++2DiOWjk3Du/GHTOyDnLokgzHSADd9Re/xq4TFVvSeB3i6puERFEZDtVvV9cEnts\nWwv66NfSv7lss4yNETlj41Z3qsPneFdjD958/O6qqmfWN1QX1DNFZFw+oJmvp8beflV1I/Cq6odi\nay7w4AckIlaTJzD37VurVu/AlcbaGdjAXFDwFHBeLM7YAkaLrYWzRasjh94/D6DKM71ggi3Vd33O\nQQvnPvq16JyLs2VMemPmc5DFFUYf5J49oEOF0yP5/DJwHG4g/SHukfi2qvr6mLYW9NWvpX9z2PZx\nbFS+fY/XFLx19SvGBSEyBJtmvyKy7bAuIrKjqv4oJMcGv0m1svZt9X1vrUTkPap6birOYpuUaLEN\noXNnrXIjF+eiVSt/eRZFmuUAWUQOBi4FHsX9p7MCeJtGLk81xOEgXP7MWlX9ZSpbC/ri19K/uWyH\n9pNsbIjIelU9dNK2EbYH46+VJSDp7Ld67Hcy7i7JYCGLx4CrgDN1wix+I1+Lxp39isghwP/BTXTZ\nCPyxqj5afbZ1Ik4spA7MLX0bSisReRmwN8+eJPu5GJxzIRRnD62WAB/AVUd5Ie4u4+PAlcAZqvpk\nC5/e56AP5z76DaFzBs55ziONlNw8DQ33CGDP2vs9cGWisnMrLW//5rLNoNEi3Gph38At8LC0arsC\n98c6XuAQ3OP3HwHrcI/IBp9tjOXXoJM3X4vGRr+345YTBlgFPAQcUL2PVjw/RN+mbiG0Ak7DTY79\nN+Bi3A/05RE5Wyb4ZZnUZNEK+AruUfvy2rbl1bZ1E2xDXOd8OPfOr0Xn3FqlbtkJRD042NRmW2n9\nbJb+zWWbQaMTgUdwtZMfrl4/Ul2k3h3reAkTkHjpDByGy6W7qmoXMHn1MG++Fo2Nfr8x9H4f4AHc\nnaFogWqIvjX47ty3obTCVZBZMNgX7k7WVyNytgSMpiDIl7NFK1wKVefPqs9DXOd8OPfOr0XnnFpZ\nx6RPi7bjaWjAZ4HPAAdX7SLgs7l5lZa/f3PZZtTqPSl1JkxA4uP3bOAa4C3AgVV7S7XtnMh8O2ts\n8YubxLh8aNsuwF3A5ohjKVdg7tW3obQCvl793YCbtS9Mvltn4WwJGC223pyNWq0D/hxYVtu2DBfU\nX9fSr+U615lzH/0G0jk1Z/OY9GmznoO8HfAunJgA/w+3/vcv8rEqCAVL/+ayzQlDzljn4xWRO4Aj\ntDZxS0R2Aa4GXqKqO0Ty67UgRAi+lU3XvEtvvyLyauCHqvqNoe1LcHdkTm/DuStCaeXh13uxjxBa\nicj5wAdxP8z/C/gZcJeqHheJs2WCn8XWvKiKp1ZZcp8tnPvoN4TOGTjnWegnVuRdWmmlTU8jff7k\nq4H9GrYvAU6J6NdrQYgQfH00TqETcMV87tsYWuHyJveNyRmXp3kmLo/4iardV21bGtE2qM5ttQrU\nf0Guc105992vUfPonFOc+41+UwqZugFHAHfiJik8BWwGnsrNq7T8/ZvLNqNWXvmTsY93XEDi4xfY\nH7gNuBf3KHFdFRjcCrwiMl9vjS1+W9hGzQuOwTlH307SCr/V4aJzjtBvZs4+WlXfSZ77bOXcR78B\ndE7KOdd5NOsLhZwNHI37D2N2c0nmLyz9m8s2F55W1WdE5NcishhX1mdFS9uYx7tbSL8af/GMcXwt\nGlv8TkKuMWrh/Bwk6Fto0EoMq4dZOYvIYbjc7q22wJWqujaWrYWzRSsRORtXqeZzzC1AswvwXhE5\nXFVPnOQfj3PQwrmnfr11zsU50bn/HMx6gPxd4Js9CmIKusHSv7lsc+EOEXk+bqLbBlzO2L+0tI15\nvOP2afH74+GLZ6AaveO4WDS2+J1WxOIcq29HoWn1MMU90Wi7WEJnzsZAJkSw6aOzRavXa3Oe6T8A\nD+KqIEyCzzkYon/75Neicy7OA6Q992Pdmp6GBqwE1uKKYr9/0HLzKi1//+aynYZG95yxaMfLmKoH\nPn6JXKO37T66ahzK7wjbXCkWQStaxO7bSVoBpwKLq9cfBr4M7B+LM27yUdN2AR6KaBuihrmPVllz\nn30499FvCJ0zcM5Se30Bs43TgZ/jZkruUGsFswFL/+ayzQIRWT94raqPquqm+rYJiHm8MuYzH79n\nAYep6o7AhcBXReSAFr7aYuQ+jBp7+x3i8AIR2Xdo80mBOHRFCL3rCNq3HlqtUtWnRORA3FLvn8Hl\nbsbivEVEVjZsXwlsiWgbQmcfrVYD54nIvSKyrmr3AZ+sPpsI4znow7mPfldj1DkD59jX9UbMeorF\nzqr6stwkCqLB0r+5bJMiUM5YkOOt/K9Q1U21zeMCEh+/C1X1HgBVvby68H9JRE6i4yP/tnwDadzZ\nb+37NwBvxF3PNwCPi8gtqvp+AFVd15VDV3j0rQ/MfWvU6jfV3zcAF6nqGhH5WETOq4ELRGQH5tIk\nVgA/ZXIgY7ENcQ511koz5T5bOPfRr0XnXJwJeF3vhFi3pqeh4f7reG1uHqVNX//mss2gUdPqRQ/j\nVi96VwKtbsBdAJdWvm8DPhHLL8YFIXz4BtLYotOd1d/jgb+oXkdf2dHC2dNfiMU+vLXC1Xn+u6pv\nnw9sx9CiKZE4LwdeUbXlbWwstoE4d9aqZrttw7YdJ9iEOAd9+rfPfjvrnItziDHp06LsdFoaLnH8\nGeBpelKOq7Q0/ZvLNqNW3jljRq0sAUlnvxhr9Br5WjS2+L0b2AmXm7eyi61xTCUNzK19a9UKd/fr\naGD36v1OTPgHLhBnSyDjE2yG4OyjVZbcZwvnPvoNpHNqznlqr8fa8bQ03N2NVwEHDVpuTqVNR//m\nss2k06bq74G4Iu1vAG6LfbwYg7dYOjOiRq+Fr0Vjo9834SbenF+9323U8QUeU1kCc9++zamVD2dL\nIGOxDaGz5/5uB/apXq8CHgIOqN63mmhqvc4ZuPfGbwidp1Wr4GMythg5G+7Oxt24VYSux92JalXM\nurTpb5b+zWWbUavB3b6/Av5rfVtkrbwDkpg6jzp2I1+LxlMZuPWRc4of+RScLYFMiiAotM4MPaIH\n9gEewNVybntn0/scDKFFH/yG0HlatQo+JmOLkbNVP66LcGuEA+wFfCk3r9Ly928u24xaWXICsxxv\nTL8xfggsGhv9Xgx8drjlHnO52ri+nVatmjhbApkUQVDoc4jMuc9G7r3xG0LnadUq9Jic9SoWW1R1\ni4ggItup6v0ismduUgXBYOnfXLa5cCzwOuDjqvqkiOwE/FlLW+/jFZGLaZhlrKpvj+nXF0a+3hob\n/V5de70IOAr4QRu/Fhg550IWrTzxKxFZrlV1AVW9R0QOxR3DSyLa5sLJuGWHt1ZTUNXvichBwLtb\n7sNynbOgT35D6GxBLq06Y9YD5O9VK7b8E65u3hPAtzNzKggHS//mss0CVf058KXa+38F/rWlueV4\nLQFJTJ1H1c705mvU2OL3ivp7EbkMuLmlXwumNdgcWRc1o1aT0MTZEsikCIKC1p9V1etGbP8pria6\ncypyhaoeM+K7lnPQG33yG0JnCyJrFXRMSnVbeuZRXRiWAGtV9Ze5+RSEhaV/c9n2EdbjFZEFwM2q\n+vup/DbV6BWR12qL+sC+fK2w+K3usq9R1ZeGZzbWb3KtLH1bfTe5VlbODfvzDmTa2obmbIGI3Kmq\nv5fa73zDtOucYkzO+h3krVDVG3NzKIgHS//msu0jAhzv7sALY/sNuHiGF98AaO1XRDbz7FSHx8iz\nel4SrSx9m0uryIu57BbDdhoWoBmB+XFXLz+mTufUY3LeBMgFBQXpkTF4W6JuKdTjgc+p6mkismmS\nUcYAytuvqmZZ5rxvfQv5tMLAuQUsgcw425icCwp8kHRMlgC5oKAgGjIGJNtUkz+OBU5pa5SLr8Wv\niKxX1UMnbQuNvvUt5NMKA+eMmFbOQfNMC0ZiGnVOOiYXxHZQUFAwfyEi69tsi4CPAF8BvqWqt4vI\nbrhasGORi6+PXxFZJCJLgR1F5AUisrRquwL/IQ7T8fymtW9za4XneGwJSyAzzjYm51ao+mrfoc05\n0odmGj3SOemYnDeT9AoKCtJBRBbhliO9HjiYuR/ixbiJdntlotaIXHwtfkXkROB9wM7A92u2TwEX\nqep508Y5F3JpFRrGCadTM9FuHJryTIGteaYFYVB0nowSIBcUFARH7oBEOtbozRhsmv2KyHtU9dwY\n/Eb461XfDtkm1arm18L5BjwDGaNtljrXg+oJVZ7pikGeqaoO3+EsMKCPOqcekyUHuaCgIDhU9Rzg\nnFwBCR1r9ObiG8Kvqp4rIi8D9sYd62D75wLRHPbXq76tI7VWNVhqRlsmJllsc9W5ntbc51lDH3VO\nOibLHeSCgoKoyBSQDHNoXaM3F19fvyJyGi7VYW/gGuBw3LGuisP0Wb771rfZtBri0YXz3cBrgUuB\nU6rcy1Z3+iy2Fs4WiMibgA9Xvt5Z5Zn+tW+t54JmzILOscdkuYNcUFAQDaMCEiBpEEXLGr25+Br9\nrgL2A+5U1eNEZBnw+UhUt6JvfVshi1YN6MJ5MDHpZo+JSRbbYSSpc62qXwS+WHv/MNCboK0vmBGd\no47JEiAXFBTERK7gzbdGb64AyuL3aVV9RkR+LSKLcXmmK2IRraFvfQuZtLJwtgQyFlujzt7Ilfs8\n39BHnVOPyRIgFxQUxESWgMRQozdXsGnxe4eIPB+4CDcR62fAv0TiWUff+hYyaWWsc22Z4Odtm7HO\nda7c5/mG3umcekyWALmgoCAmsgQk4r8gRK5g09uvqr6zevlpEVkLLK6X8oqIvvVtNq0snLEFMt62\nRs7eUNUrhnxehkvdKQiIPuqcekyWSXoFBQVJIG5RhqgBScgavSn4hvCbK5AZ8rcrPejb5D+wEWpG\nWyYmtbGNwdkCEdkTWKOqL03pd75hmnXONSbLHeSCgoJoqAcfqvro8LYIeAdzNXo38OwavW1qCqfm\n6+239qOxo7hFIOo/GklW0utL32bUyjQeR8AyMamNbQzOrZEr93m+oWc6ZxmT5Q5yQUFBcOS+C9W1\nRm8uvha/0rxghwKbgQtV9VPTxjmQ/871l3NpVfPvXTN6RCDzgeFH5BFsc9W5LihoROoxWQLkgoKC\n4MgdkFQcWtfozRhsmv2KyKnA2dWCEB8G9gc+qqobp5VzAA6+NaOTajXkO3vN6K7IwXkaUobmA/qq\nc8oxuSDGTgsKCuY3VPUcVX0xcDrwu9Xri4GHSTOR6zTg3KodApyFW3J3qvgG8ruqCvgOBP4Q+Axw\nQRTC9K9vh5BUqwEsnEVkfZttEWwtOneGiCwSkaVUaTAisrRqu5IgZWi+oM86px6TJUAuKCiIiSwB\nCa5G76HAY6p6HK5e75I2drn4Gvz+pvr7BuAiVV0DLIzAcRh961vIqBUdOVsCmUBBkEVnH7wDl1+6\nV/V30K4kQe7zPEKfdU46JkuAXFBQEBO5ApKnVfUZoGuN3lx8LX6/LyJ/B7wZuEZEtiPNtb1vfQv5\ntPLhbAlkQgRBFp07o/Zk4n+r6m6q+uKq7aeq0x649QY91znpmCxVLAoKCmJiEJC8BjgzYUDiW6M3\nF1+L32OB1wEfV9UnRWQn4M8i8ayjb30L+bTqzFlVzwHO8ZmYZLG1cA4BVT23j/nafUNPdU46Jssk\nvYKCgmgQke1xAcndqvpQFZC8XFXXJeSwKy1r9ObiOw06dcU0cO7St9MCH86WQCZEEJRS5yrP9GAc\n52uAw3G1m1fF9j2f0HedU4zJEiAXFBTMHPo6Q7tgMvrYtxbOlkDGaJtFZxG5G5dbeqeq7iciy4DP\nq+prYvqdb+ijzqnHZEmxKCgomBlI5sUzCuKhj30biPMq5gKZ4waBTCzbKdD5aVV9RkSS5JnOY/RG\n51xjsgTIBQUFs4SmFZcGNXrLogf9Rh/7NgRnSyDjY5tb5yy5z/MQfdI5y5gsVSwKCgpmBrlr9BbE\nQx/7NhDn4UBmY0zb3Dqr6jtV9UlV/TRuAujbqpJeBQHRJ51zjckSIBcUFMwictXoLYiPPvatN2dL\nIGMMgnItqrJ1IRNVfVRVN7Vd3KSgPXqqc9IxWQLkgoKCWUSuGr0F8dHHvvXmbAlkjEFQUp37vMJb\nn9BznZOOyZKDXFBQMIvIVaO3ID762LedOVsmJgWa1JRa59y5z/MFfdY56ZgsZd4KCgpmDtNQo7cg\nDvrYtz6cReRE5gKZ7/PsQOZCVf1UDFsL5xAQkVOBs6tH6R8G9gc+qqobY/qdb+ijzqnHZAmQCwoK\nCgoKphSWQKanQdAmVd23yjP9KPBx4FRVfVVmajOFovNkTPtjqYKCgoKCgvkMy8SkPk5o7GOOeR9R\ndJ6AEiAXFBQUFBRMLyyBTB+DoEGe6ZuBa3qSY95HFJ0noKRYFBQUFBQUTClE5GpcHvFrcCkSTwNf\nV9X9YtrmQh9zzPuIovNklAC5oKCgoKBgSmEJZEoQVFDgjxIgFxQUFBQUFBQUFNRQ8k0KCgoKCgoK\nCgoKaigBckFBQUFBQUFBQUENJUAuKCgoKCgoKCgoqKEEyAUFBQUFBQUFBQU1/H/OzHj4674kuwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e61043c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ss = sorted(d, key=d.get, reverse=True)\n",
    "top_names = ss[0:n_features]\n",
    "\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(n_features), [d[i] for i in top_names], color=\"r\", align=\"center\")\n",
    "plt.xlim(-1, n_features)\n",
    "plt.xticks(range(n_features), top_names, rotation='vertical')\n",
    "plt.yticks(np.arange(0, 0.12, 0.005))\n",
    "plot_value_labels(plt.gca(),format='{:.3f}')\n",
    "plt.gcf().set_size_inches(10,6)\n",
    "plt.ylim(0.0,0.11)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## can we do better by training a different model by subpopulation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30000.000000\n",
       "mean        35.485500\n",
       "std          9.217904\n",
       "min         21.000000\n",
       "25%         28.000000\n",
       "50%         34.000000\n",
       "75%         41.000000\n",
       "max         79.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### young people (age<=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/+rO6qSkXDhawS0qCUaPg4YehuiV0U3EUNXx08C7mJsAmVT0gIqcAnYH3cIXx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\nScEYY4zP/wMPhFqdQJyzbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e45c1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['age']<=30].drop('default',axis=1)\n",
    "target = df[df['age']<=30]['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> solid gains here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## middle age (30 < age <=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i11EZU2hyaz46MIu5AbBRVfeLyAlAB+BDXGE8EwS//buVjEylX7vaNlu5uNi5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e45e6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['age'].between(31,50)].drop('default',axis=1)\n",
    "target = df[df['age'].between(31,50)]['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## older people (age > 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nB8aNc2sk33WXK143YICVqDAVTrFJQUROBt4FNgMCNBGRK1X1V38HF0la1Le1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\nAZEgFii5/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e45e6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['age'] > 50].drop('default',axis=1)\n",
    "target = df[df['age'] > 50]['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## men only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6NLCEUNjt2QNnnOEK2I0YAbVqwXnneR2VMfkukCGpk3FLcZ4FvAU0AD4MalRF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\nJQVjjDF+lhSMMcb4WVIwxhjj93+d1W9+4U2IeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e47dc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['sex_1'] == 1].drop('default',axis=1)\n",
    "target = df[df['sex_1'] == 1]['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## women only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XEIwpAXLrPjq85GbtogjEHLH7wCGufNPd/HVVzybWZVScHC5gFxMDo0fDf/8L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\njDHG+P0/NaSdFaGIgY4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e49b14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['sex_2'] == 1].drop('default',axis=1)\n",
    "target = df[df['sex_2'] == 1]['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.values, \n",
    "    target.values, \n",
    "    test_size=0.25)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "# take the second column because the classifier outputs scores for\n",
    "# the 0 class as well\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  }
 ],
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